ggplot
Naomi Tague
Feb 2, 2016
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## dateGGPLOT is a “grammer of graphics”, in other words a set of elements (like words, phrases) that you can put together to make a plot. A “grammer” is more flexible than standard plotting; but a bit harder to learn In ggplot, we combine the following element:
data a data set used to generate the plot
geoms visual character of the plot- boxplot, scatter plot
aesthetics what gets put into the geom
themes characteristics of plotting environment (axis label font…)
GGPLOT is organized in a way that you can build a very complex plot by adding pieces to it
A simple example
# basic plot
ggplot(clim, aes(y=tmax, x=month))+geom_point()
# would look bettter as a boxplot
ggplot(clim, aes(y=tmax, x=as.factor(month)))+geom_boxplot()
# save the basic plot so we can add to it
p=ggplot(clim, aes(y=tmax, x=as.factor(month)))+geom_boxplot()
p+labs(y="Maximum Temperature", x="Month")
p = p + labs(y="Maximum Temperature", x="Month")
p Thre are many ways to do this (see labs and titles) but themes give you alot of control on your plot elements * plot.title * axis.title * axis.title.x * axis.title.y * axis.text * axis.text.y
I’ll also show you the use of expression to write math symbols as labs
More on all the options that can be found with [themes]{http://ggplot2.tidyverse.org/reference/theme.html}
# themes are used to control character of the plot
pclr =ggplot(clim, aes(y=tmax, x=as.factor(month)))+geom_boxplot()
pclr = pclr + labs(y="Maximum Temperature", x="Month")
pclr = pclr+theme(axis.text= element_text(face="bold", colour="red", size=14))
pclr = pclr + geom_hline(yintercept=0, col="yellow", size=4)
pclr
#lets say we want to color in the boxes - this is inside the boxplto so can't add
p = ggplot(clim, aes(y=tmax, x=as.factor(month)))+geom_boxplot(col="rosybrown3", fill="red")
p
# useful to see color options
colors()## [1] "white" "aliceblue" "antiquewhite"
## [4] "antiquewhite1" "antiquewhite2" "antiquewhite3"
## [7] "antiquewhite4" "aquamarine" "aquamarine1"
## [10] "aquamarine2" "aquamarine3" "aquamarine4"
## [13] "azure" "azure1" "azure2"
## [16] "azure3" "azure4" "beige"
## [19] "bisque" "bisque1" "bisque2"
## [22] "bisque3" "bisque4" "black"
## [25] "blanchedalmond" "blue" "blue1"
## [28] "blue2" "blue3" "blue4"
## [31] "blueviolet" "brown" "brown1"
## [34] "brown2" "brown3" "brown4"
## [37] "burlywood" "burlywood1" "burlywood2"
## [40] "burlywood3" "burlywood4" "cadetblue"
## [43] "cadetblue1" "cadetblue2" "cadetblue3"
## [46] "cadetblue4" "chartreuse" "chartreuse1"
## [49] "chartreuse2" "chartreuse3" "chartreuse4"
## [52] "chocolate" "chocolate1" "chocolate2"
## [55] "chocolate3" "chocolate4" "coral"
## [58] "coral1" "coral2" "coral3"
## [61] "coral4" "cornflowerblue" "cornsilk"
## [64] "cornsilk1" "cornsilk2" "cornsilk3"
## [67] "cornsilk4" "cyan" "cyan1"
## [70] "cyan2" "cyan3" "cyan4"
## [73] "darkblue" "darkcyan" "darkgoldenrod"
## [76] "darkgoldenrod1" "darkgoldenrod2" "darkgoldenrod3"
## [79] "darkgoldenrod4" "darkgray" "darkgreen"
## [82] "darkgrey" "darkkhaki" "darkmagenta"
## [85] "darkolivegreen" "darkolivegreen1" "darkolivegreen2"
## [88] "darkolivegreen3" "darkolivegreen4" "darkorange"
## [91] "darkorange1" "darkorange2" "darkorange3"
## [94] "darkorange4" "darkorchid" "darkorchid1"
## [97] "darkorchid2" "darkorchid3" "darkorchid4"
## [100] "darkred" "darksalmon" "darkseagreen"
## [103] "darkseagreen1" "darkseagreen2" "darkseagreen3"
## [106] "darkseagreen4" "darkslateblue" "darkslategray"
## [109] "darkslategray1" "darkslategray2" "darkslategray3"
## [112] "darkslategray4" "darkslategrey" "darkturquoise"
## [115] "darkviolet" "deeppink" "deeppink1"
## [118] "deeppink2" "deeppink3" "deeppink4"
## [121] "deepskyblue" "deepskyblue1" "deepskyblue2"
## [124] "deepskyblue3" "deepskyblue4" "dimgray"
## [127] "dimgrey" "dodgerblue" "dodgerblue1"
## [130] "dodgerblue2" "dodgerblue3" "dodgerblue4"
## [133] "firebrick" "firebrick1" "firebrick2"
## [136] "firebrick3" "firebrick4" "floralwhite"
## [139] "forestgreen" "gainsboro" "ghostwhite"
## [142] "gold" "gold1" "gold2"
## [145] "gold3" "gold4" "goldenrod"
## [148] "goldenrod1" "goldenrod2" "goldenrod3"
## [151] "goldenrod4" "gray" "gray0"
## [154] "gray1" "gray2" "gray3"
## [157] "gray4" "gray5" "gray6"
## [160] "gray7" "gray8" "gray9"
## [163] "gray10" "gray11" "gray12"
## [166] "gray13" "gray14" "gray15"
## [169] "gray16" "gray17" "gray18"
## [172] "gray19" "gray20" "gray21"
## [175] "gray22" "gray23" "gray24"
## [178] "gray25" "gray26" "gray27"
## [181] "gray28" "gray29" "gray30"
## [184] "gray31" "gray32" "gray33"
## [187] "gray34" "gray35" "gray36"
## [190] "gray37" "gray38" "gray39"
## [193] "gray40" "gray41" "gray42"
## [196] "gray43" "gray44" "gray45"
## [199] "gray46" "gray47" "gray48"
## [202] "gray49" "gray50" "gray51"
## [205] "gray52" "gray53" "gray54"
## [208] "gray55" "gray56" "gray57"
## [211] "gray58" "gray59" "gray60"
## [214] "gray61" "gray62" "gray63"
## [217] "gray64" "gray65" "gray66"
## [220] "gray67" "gray68" "gray69"
## [223] "gray70" "gray71" "gray72"
## [226] "gray73" "gray74" "gray75"
## [229] "gray76" "gray77" "gray78"
## [232] "gray79" "gray80" "gray81"
## [235] "gray82" "gray83" "gray84"
## [238] "gray85" "gray86" "gray87"
## [241] "gray88" "gray89" "gray90"
## [244] "gray91" "gray92" "gray93"
## [247] "gray94" "gray95" "gray96"
## [250] "gray97" "gray98" "gray99"
## [253] "gray100" "green" "green1"
## [256] "green2" "green3" "green4"
## [259] "greenyellow" "grey" "grey0"
## [262] "grey1" "grey2" "grey3"
## [265] "grey4" "grey5" "grey6"
## [268] "grey7" "grey8" "grey9"
## [271] "grey10" "grey11" "grey12"
## [274] "grey13" "grey14" "grey15"
## [277] "grey16" "grey17" "grey18"
## [280] "grey19" "grey20" "grey21"
## [283] "grey22" "grey23" "grey24"
## [286] "grey25" "grey26" "grey27"
## [289] "grey28" "grey29" "grey30"
## [292] "grey31" "grey32" "grey33"
## [295] "grey34" "grey35" "grey36"
## [298] "grey37" "grey38" "grey39"
## [301] "grey40" "grey41" "grey42"
## [304] "grey43" "grey44" "grey45"
## [307] "grey46" "grey47" "grey48"
## [310] "grey49" "grey50" "grey51"
## [313] "grey52" "grey53" "grey54"
## [316] "grey55" "grey56" "grey57"
## [319] "grey58" "grey59" "grey60"
## [322] "grey61" "grey62" "grey63"
## [325] "grey64" "grey65" "grey66"
## [328] "grey67" "grey68" "grey69"
## [331] "grey70" "grey71" "grey72"
## [334] "grey73" "grey74" "grey75"
## [337] "grey76" "grey77" "grey78"
## [340] "grey79" "grey80" "grey81"
## [343] "grey82" "grey83" "grey84"
## [346] "grey85" "grey86" "grey87"
## [349] "grey88" "grey89" "grey90"
## [352] "grey91" "grey92" "grey93"
## [355] "grey94" "grey95" "grey96"
## [358] "grey97" "grey98" "grey99"
## [361] "grey100" "honeydew" "honeydew1"
## [364] "honeydew2" "honeydew3" "honeydew4"
## [367] "hotpink" "hotpink1" "hotpink2"
## [370] "hotpink3" "hotpink4" "indianred"
## [373] "indianred1" "indianred2" "indianred3"
## [376] "indianred4" "ivory" "ivory1"
## [379] "ivory2" "ivory3" "ivory4"
## [382] "khaki" "khaki1" "khaki2"
## [385] "khaki3" "khaki4" "lavender"
## [388] "lavenderblush" "lavenderblush1" "lavenderblush2"
## [391] "lavenderblush3" "lavenderblush4" "lawngreen"
## [394] "lemonchiffon" "lemonchiffon1" "lemonchiffon2"
## [397] "lemonchiffon3" "lemonchiffon4" "lightblue"
## [400] "lightblue1" "lightblue2" "lightblue3"
## [403] "lightblue4" "lightcoral" "lightcyan"
## [406] "lightcyan1" "lightcyan2" "lightcyan3"
## [409] "lightcyan4" "lightgoldenrod" "lightgoldenrod1"
## [412] "lightgoldenrod2" "lightgoldenrod3" "lightgoldenrod4"
## [415] "lightgoldenrodyellow" "lightgray" "lightgreen"
## [418] "lightgrey" "lightpink" "lightpink1"
## [421] "lightpink2" "lightpink3" "lightpink4"
## [424] "lightsalmon" "lightsalmon1" "lightsalmon2"
## [427] "lightsalmon3" "lightsalmon4" "lightseagreen"
## [430] "lightskyblue" "lightskyblue1" "lightskyblue2"
## [433] "lightskyblue3" "lightskyblue4" "lightslateblue"
## [436] "lightslategray" "lightslategrey" "lightsteelblue"
## [439] "lightsteelblue1" "lightsteelblue2" "lightsteelblue3"
## [442] "lightsteelblue4" "lightyellow" "lightyellow1"
## [445] "lightyellow2" "lightyellow3" "lightyellow4"
## [448] "limegreen" "linen" "magenta"
## [451] "magenta1" "magenta2" "magenta3"
## [454] "magenta4" "maroon" "maroon1"
## [457] "maroon2" "maroon3" "maroon4"
## [460] "mediumaquamarine" "mediumblue" "mediumorchid"
## [463] "mediumorchid1" "mediumorchid2" "mediumorchid3"
## [466] "mediumorchid4" "mediumpurple" "mediumpurple1"
## [469] "mediumpurple2" "mediumpurple3" "mediumpurple4"
## [472] "mediumseagreen" "mediumslateblue" "mediumspringgreen"
## [475] "mediumturquoise" "mediumvioletred" "midnightblue"
## [478] "mintcream" "mistyrose" "mistyrose1"
## [481] "mistyrose2" "mistyrose3" "mistyrose4"
## [484] "moccasin" "navajowhite" "navajowhite1"
## [487] "navajowhite2" "navajowhite3" "navajowhite4"
## [490] "navy" "navyblue" "oldlace"
## [493] "olivedrab" "olivedrab1" "olivedrab2"
## [496] "olivedrab3" "olivedrab4" "orange"
## [499] "orange1" "orange2" "orange3"
## [502] "orange4" "orangered" "orangered1"
## [505] "orangered2" "orangered3" "orangered4"
## [508] "orchid" "orchid1" "orchid2"
## [511] "orchid3" "orchid4" "palegoldenrod"
## [514] "palegreen" "palegreen1" "palegreen2"
## [517] "palegreen3" "palegreen4" "paleturquoise"
## [520] "paleturquoise1" "paleturquoise2" "paleturquoise3"
## [523] "paleturquoise4" "palevioletred" "palevioletred1"
## [526] "palevioletred2" "palevioletred3" "palevioletred4"
## [529] "papayawhip" "peachpuff" "peachpuff1"
## [532] "peachpuff2" "peachpuff3" "peachpuff4"
## [535] "peru" "pink" "pink1"
## [538] "pink2" "pink3" "pink4"
## [541] "plum" "plum1" "plum2"
## [544] "plum3" "plum4" "powderblue"
## [547] "purple" "purple1" "purple2"
## [550] "purple3" "purple4" "red"
## [553] "red1" "red2" "red3"
## [556] "red4" "rosybrown" "rosybrown1"
## [559] "rosybrown2" "rosybrown3" "rosybrown4"
## [562] "royalblue" "royalblue1" "royalblue2"
## [565] "royalblue3" "royalblue4" "saddlebrown"
## [568] "salmon" "salmon1" "salmon2"
## [571] "salmon3" "salmon4" "sandybrown"
## [574] "seagreen" "seagreen1" "seagreen2"
## [577] "seagreen3" "seagreen4" "seashell"
## [580] "seashell1" "seashell2" "seashell3"
## [583] "seashell4" "sienna" "sienna1"
## [586] "sienna2" "sienna3" "sienna4"
## [589] "skyblue" "skyblue1" "skyblue2"
## [592] "skyblue3" "skyblue4" "slateblue"
## [595] "slateblue1" "slateblue2" "slateblue3"
## [598] "slateblue4" "slategray" "slategray1"
## [601] "slategray2" "slategray3" "slategray4"
## [604] "slategrey" "snow" "snow1"
## [607] "snow2" "snow3" "snow4"
## [610] "springgreen" "springgreen1" "springgreen2"
## [613] "springgreen3" "springgreen4" "steelblue"
## [616] "steelblue1" "steelblue2" "steelblue3"
## [619] "steelblue4" "tan" "tan1"
## [622] "tan2" "tan3" "tan4"
## [625] "thistle" "thistle1" "thistle2"
## [628] "thistle3" "thistle4" "tomato"
## [631] "tomato1" "tomato2" "tomato3"
## [634] "tomato4" "turquoise" "turquoise1"
## [637] "turquoise2" "turquoise3" "turquoise4"
## [640] "violet" "violetred" "violetred1"
## [643] "violetred2" "violetred3" "violetred4"
## [646] "wheat" "wheat1" "wheat2"
## [649] "wheat3" "wheat4" "whitesmoke"
## [652] "yellow" "yellow1" "yellow2"
## [655] "yellow3" "yellow4" "yellowgreen"p = p + labs(y= expression(paste("Maximum Temperature ",C**degree)), x="Month")
p= p+theme(axis.text= element_text(face="bold", colour="steelblue", size=14),
axis.title=element_text(face="italic", size=14))
p 
p = p+ ggtitle("Monthly Temperature in Sierras")
p
# notice how themes keep inheriting - so you can call theme multiple times
p + coord_flip() 
p=p + coord_flip() + theme(plot.margin=unit(c(1,3,1,3),"cm")) # top right bottom left
p
p = p + theme(plot.title=element_text(size=16, hjust=0.5), plot.background=element_rect(fill="peachpuff1"))
p
# to display more than one plot (a "matrix" or grid of plots)
grid.arrange(p,pclr)
# or to control to make by col
grid.arrange(p, pclr, ncol=2)
# there are also built in themes
pclr = pclr+theme_bw()
pclr
There are many different geoms (geometries) - lets try the standard ones
# scatter plot
p2=ggplot(clim, aes(x=tmax, y=tmin))+geom_point(col="blue", shape=9, size=rel(4))
p2
p2=p2+labs(x="Max Temp C", y="Min Temp C")
p2 = p2+ ggtitle("How does daily maximim and min temp compare")
p2 = p2 + geom_abline(intercept=0,slope=1, colour="yellow", size=4)
p2
#density plot
p3=ggplot(clim, aes(x=rain))+geom_density()
p3
p3 = ggplot(subset(clim, clim$rain > 0), aes(x=rain))+geom_density(fill="blue")
#p4=ggplot(clim, aes(x=date,y=rain))+geom_line()+ggtitle("Line Graph")
p3
#fix issue with date
clim$date = mdy(paste(clim$month, clim$day, clim$year, sep="/"))
p4=ggplot(clim, aes(x=date,y=rain))+geom_line()+ggtitle("Line Graph")
p4
grid.arrange(p,p2,p3,p4)
One of the most useful things about GGPLOT is that it makes it easy to visualize your data in ways that highlight different attributes - this can help you to see multiple dimensions at once There are multiple ways to do this
- color/size/shape (within plots)
- facets (between plots)
Lets start with color We can use a more interesting data set thindeep This dataset is results of a simulation experiment that looks at the impacts of different levels of forest thinning on water and carbon fluxes for a site in the California Sierra The site/plot is simulated for 10 years following the thinning experiment. The thinning experiment is repeated for different start dates within a 50 year historic climate record, for different thinning intensities and for different types of thinning that try to maximize or minimize sharing of water between trees
# ok lets look at how biomass recovers
p1=ggplot(thindeep, aes(x=as.factor(wy), y=plantc))+geom_boxplot()
p1 = p1+labs(x="Years since thinning", y="Biomass")
p1 = p1 +theme(axis.text= element_text(face="bold", size=14),
plot.margin = unit(c(15,15,15,5),"pt"), axis.title = element_text(size=14))
p1
# but boxplots include all thinning intensities - maybe we want to separate those out
p1=ggplot(thindeep, aes(x=as.factor(wy), y=plantc, col=as.factor(thin)))+geom_boxplot()
p1 = p1+labs(x="Years since thinning", y="Biomass")
p1 = p1 +theme(axis.text= element_text(face="bold", size=14),
plot.margin = unit(c(15,15,15,5),"pt"), axis.title = element_text(size=14))
# or we could base the filling of the boxplots on thinning intensity
p1=ggplot(thindeep, aes(x=as.factor(wy), y=plantc, fill=as.factor(thin)))+geom_boxplot()
p1 = p1+labs(x="Years since thinning", y="Biomass")
p1 = p1 +theme(axis.text= element_text(face="bold", size=14),
plot.margin = unit(c(15,15,15,5),"pt"), axis.title = element_text(size=14))This is a good opporutnity to play witht e legend a little, we can use theme again to control formatting and scale_color_discrete to control content There are many other ways to change color maps - see
- scale_colour_hue
- scale_fill_hue
- scale_color_brewer
- scale_color_brewer
Brewer is really cool and comes up with nice options for you and gives you a menu of color scheme for sequential vs divergin vs qualitative data and has optins that are printer friendly, color blind friendly -to check it out - [colors that work]{http://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3} [implemented R color palettes]{https://www.r-bloggers.com/choosing-colour-palettes-part-ii-educated-choices/} [more R and colors]{http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/} or for continues * scale_color_continuous
# assuming qualitative
p1 + scale_fill_brewer(type="qual", palette="Dark2")
p1 + scale_fill_brewer(type="qual", palette="Set3")
# but actually sequential so can using diverging palettes - diverse from center
p1 + scale_fill_brewer(type="div", palette="PiYG")
# but actually sequential so can using diverging palettes - diverse from center
p1 + scale_fill_brewer(type="seq", palette="BuGn")
# and can add names
lnms = c("None","Low","Med","High")
p1 + scale_fill_brewer(type="seq", palette="BuGn", name="Thin Intensity", labels=lnms)
# using themes to change legend format - position
p1 + scale_fill_brewer(type="seq", palette="BuGn", name="Thin Intensity", labels=lnms) +
theme(legend.position="bottom")
p1 + scale_fill_brewer(type="seq", palette="BuGn", name="Thin Intensity", labels=lnms) +
theme(legend.position=c(0.1,0.9))
p1 + scale_fill_brewer(type="seq", palette="BuGn", name="Thin Intensity", labels=lnms) +
theme(legend.position=c(0.1,0.9), legend.background=element_rect(fill="seashell2")) 
We can also use ggplot to automatically summarize graph and added to our plot Lets say we also want to show the means as lines
# what if we just want means
# there are multiple ways to do this but
p1=ggplot(thindeep, aes(x=wy, y=plantc, col=as.factor(thin)))+stat_summary(fun.y="mean", geom="line")
p1 = p1 +theme(axis.text= element_text(face="bold", size=14))
p1 + scale_color_brewer(type="div", palette="Spectral", name="Thin Intensity", labels=lnms) +
theme(legend.position=c(0.1,0.9), legend.background=element_rect(fill="seashell2")) 
# we also might want to separate out our two different types of thinning - indicated by shared
p1=ggplot(thindeep, aes(x=wy, y=plantc, col=as.factor(thin), type=shared))+stat_summary(fun.y="mean", geom="line", aes(linetype=shared))
p1 = p1 +theme(axis.text= element_text(face="bold", size=14))
p1 + scale_color_brewer(type="div", palette="Spectral", name="Thin Intensity", labels=lnms) +
scale_linetype(name="Thin Type", labels=c("Spaced","Clustered"))
#averaging by year and then fitting a curve
p1 = ggplot(clim, aes(y=tmax,x=year))+stat_summary(fun.y="mean", geom="point")+stat_smooth()
# we can also combined bar plots and line summaries (and deal with different axis)
# we need to transform to get similar scales
p1 = ggplot(clim) + geom_bar(aes(x=year,y=rain), stat="summary", fun.y="sum") + ggtitle("Rain")
p2 = ggplot(clim) + geom_line(aes(x=year, y=tmax), stat="summary", fun.y="mean") + ggtitle("Temp")
grid.arrange(p1,p2)
# estimate scaling
scl = 3000/15
# add some labels
p = ggplot(clim) + geom_bar(aes(x=year,y=rain), stat="summary", fun.y="sum", fill="cyan") +
geom_line(aes(x=year, y=tmax*scl), stat="summary", fun.y="mean", col="red")
p 
p=p+ scale_y_continuous(sec.axis = sec_axis(~./scl, name=expression(paste("Maximum Temperature ",C**degree)))) +
labs(x="Year", y="Rainfall (mm/yr)")
p
# annotation
p = ggplot(clim) + geom_bar(aes(x=year,y=rain), stat="summary", fun.y="sum", fill="cyan") +
geom_line(aes(x=year, y=tmax*scl), stat="summary", fun.y="mean", col="red")
p=p+ scale_y_continuous(sec.axis = sec_axis(~./scl, name=expression(paste("Maximum Temperature ",C**degree)))) +
labs(x="Year", y="Rainfall (mm/yr)")
p = p+ annotate("text", x=1990, y=500, label="Precip", colour="blue", size=6, hjust=0.5)
p = p+ annotate("text", x=1940, y=2700, label="Temperature", colour="red", size=6, hjust=0)
p
Now lets try facets which allow you to separate things into different graphs lets say we wanted a separate graph for each thinning intensity so we could see how decade impacted recovery
# first lets color by decade/scenario
p2 = ggplot(thindeep, aes(x=wy, y=plantc, col=as.factor(scen)))+ stat_summary(fun.y="mean",geom="line", aes(col=as.factor(scen)))
p2
# now lets separate out the thinning intensities
p2= p2+facet_wrap(~as.factor(thin))
p2
# make prettier
p2 = ggplot(thindeep, aes(x=wy, y=plantc, col=as.factor(scen)))+ stat_summary(fun.y="mean",geom="line", size=2, aes(col=as.factor(scen)))
p2= p2+facet_wrap(~as.factor(thin))
p2 + scale_color_brewer(type="qual", palette="Set3", name="Decade") 
p2